{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7765UFHoyGx6"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "KVtTDrUNyL7x"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "r0_fqL3ayLHX"
      },
      "source": [
        "# Gradient Boosted Trees: Model understanding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PS6_yKSoyLAl"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/estimator/boosted_trees_model_understanding\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/estimator/boosted_trees_model_understanding.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/estimator/boosted_trees_model_understanding.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/estimator/boosted_trees_model_understanding.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dW3r7qVxzqN5"
      },
      "source": [
        "For an end-to-end walkthrough of training a Gradient Boosting model check out the [boosted trees tutorial](./boosted_trees). In this tutorial you will:\n",
        "\n",
        "* Learn how to interpret a Boosted Trees model both *locally* and *globally*\n",
        "* Gain intution for how a Boosted Trees model fits a dataset\n",
        "\n",
        "## How to interpret Boosted Trees models both locally and globally\n",
        "\n",
        "Local interpretability refers to an understanding of a model’s predictions at the individual example level, while global interpretability refers to an understanding of the model as a whole. Such techniques can help machine learning (ML) practitioners detect bias and bugs during the model development stage.\n",
        "\n",
        "For local interpretability, you will learn how to create and visualize per-instance contributions. To distinguish this from feature importances, we refer to these values as directional feature contributions (DFCs).\n",
        "\n",
        "For global interpretability you will retrieve and visualize gain-based feature importances, [permutation feature importances](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) and also show aggregated DFCs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eylrTPAN3rJV"
      },
      "source": [
        "## Load the titanic dataset\n",
        "You will be using the titanic dataset, where the (rather morbid) goal is to predict passenger survival, given characteristics such as gender, age, class, etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "KuhAiPfZ3rJW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Load dataset.\n",
        "dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')\n",
        "dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')\n",
        "y_train = dftrain.pop('survived')\n",
        "y_eval = dfeval.pop('survived')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "sp1ShjJJeyH3",
        "outputId": "c7c3a79e-f5d1-46a4-ec64-b26bdf65c28c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "tf.random.set_seed(123)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3ioodHdVJVdA"
      },
      "source": [
        "For a description of the features, please review the prior tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "krkRHuMp3rJn"
      },
      "source": [
        "## Create feature columns, input_fn, and the train the estimator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JiJ6K3hr1lXW"
      },
      "source": [
        "### Preprocess the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "udMytRJC05oW"
      },
      "source": [
        "Create the feature columns, using the original numeric columns as is and one-hot-encoding categorical variables."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "upaNWxcF3rJn"
      },
      "outputs": [],
      "source": [
        "fc = tf.feature_column\n",
        "CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',\n",
        "                       'embark_town', 'alone']\n",
        "NUMERIC_COLUMNS = ['age', 'fare']\n",
        "\n",
        "def one_hot_cat_column(feature_name, vocab):\n",
        "  return fc.indicator_column(\n",
        "      fc.categorical_column_with_vocabulary_list(feature_name,\n",
        "                                                 vocab))\n",
        "feature_columns = []\n",
        "for feature_name in CATEGORICAL_COLUMNS:\n",
        "  # Need to one-hot encode categorical features.\n",
        "  vocabulary = dftrain[feature_name].unique()\n",
        "  feature_columns.append(one_hot_cat_column(feature_name, vocabulary))\n",
        "\n",
        "for feature_name in NUMERIC_COLUMNS:\n",
        "  feature_columns.append(fc.numeric_column(feature_name,\n",
        "                                           dtype=tf.float32))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9rTefnXe1n0v"
      },
      "source": [
        "### Build the input pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-UOlROp33rJo"
      },
      "source": [
        "Create the input functions using the `from_tensor_slices` method in the [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) API to read in data directly from Pandas."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "9dquwCQB3rJp"
      },
      "outputs": [],
      "source": [
        "# Use entire batch since this is such a small dataset.\n",
        "NUM_EXAMPLES = len(y_train)\n",
        "\n",
        "def make_input_fn(X, y, n_epochs=None, shuffle=True):\n",
        "  def input_fn():\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((X.to_dict(orient='list'), y))\n",
        "    if shuffle:\n",
        "      dataset = dataset.shuffle(NUM_EXAMPLES)\n",
        "    # For training, cycle thru dataset as many times as need (n_epochs=None).\n",
        "    dataset = (dataset\n",
        "      .repeat(n_epochs)\n",
        "      .batch(NUM_EXAMPLES))\n",
        "    return dataset\n",
        "  return input_fn\n",
        "\n",
        "# Training and evaluation input functions.\n",
        "train_input_fn = make_input_fn(dftrain, y_train)\n",
        "eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HttfNNlN3rJr"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "colab_type": "code",
        "id": "tgEzMtlw3rJu",
        "outputId": "bf61dabe-cc82-4163-82ce-76568f04cc87"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.806818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy_baseline</th>\n",
              "      <td>0.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>auc</th>\n",
              "      <td>0.866606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>auc_precision_recall</th>\n",
              "      <td>0.849128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>average_loss</th>\n",
              "      <td>0.421549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label/mean</th>\n",
              "      <td>0.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loss</th>\n",
              "      <td>0.421549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>precision</th>\n",
              "      <td>0.755319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction/mean</th>\n",
              "      <td>0.384944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>recall</th>\n",
              "      <td>0.717172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>global_step</th>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               0\n",
              "accuracy                0.806818\n",
              "accuracy_baseline       0.625000\n",
              "auc                     0.866606\n",
              "auc_precision_recall    0.849128\n",
              "average_loss            0.421549\n",
              "label/mean              0.375000\n",
              "loss                    0.421549\n",
              "precision               0.755319\n",
              "prediction/mean         0.384944\n",
              "recall                  0.717172\n",
              "global_step           100.000000"
            ]
          },
          "execution_count": 6,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "params = {\n",
        "  'n_trees': 50,\n",
        "  'max_depth': 3,\n",
        "  'n_batches_per_layer': 1,\n",
        "  # You must enable center_bias = True to get DFCs. This will force the model to\n",
        "  # make an initial prediction before using any features (e.g. use the mean of\n",
        "  # the training labels for regression or log odds for classification when\n",
        "  # using cross entropy loss).\n",
        "  'center_bias': True\n",
        "}\n",
        "\n",
        "est = tf.estimator.BoostedTreesClassifier(feature_columns, **params)\n",
        "# Train model.\n",
        "est.train(train_input_fn, max_steps=100)\n",
        "\n",
        "# Evaluation.\n",
        "results = est.evaluate(eval_input_fn)\n",
        "clear_output()\n",
        "pd.Series(results).to_frame()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JgAz3jDa_tRA"
      },
      "source": [
        "For performance reasons, when your data fits in memory, we recommend use the `boosted_trees_classifier_train_in_memory` function. However if training time is not of a concern or if you have a very large dataset and want to do distributed training, use the `tf.estimator.BoostedTrees` API shown above.\n",
        "\n",
        "\n",
        "When using this method, you should not batch your input data, as the method operates on the entire dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "colab_type": "code",
        "id": "y7ztzoSk_vjY",
        "outputId": "52c0d1fa-1373-4ef1-caa1-32eda73f622d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpec8e696f\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpec8e696f', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "WARNING:tensorflow:Issue encountered when serializing resources.\n",
            "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
            "'_Resource' object has no attribute 'name'\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "WARNING:tensorflow:Issue encountered when serializing resources.\n",
            "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
            "'_Resource' object has no attribute 'name'\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpec8e696f/model.ckpt.\n",
            "WARNING:tensorflow:Issue encountered when serializing resources.\n",
            "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
            "'_Resource' object has no attribute 'name'\n",
            "INFO:tensorflow:loss = 0.6931472, step = 0\n",
            "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n",
            "INFO:tensorflow:global_step/sec: 80.2732\n",
            "INFO:tensorflow:loss = 0.34654337, step = 99 (1.249 sec)\n",
            "INFO:tensorflow:Saving checkpoints for 153 into /tmp/tmpec8e696f/model.ckpt.\n",
            "WARNING:tensorflow:Issue encountered when serializing resources.\n",
            "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
            "'_Resource' object has no attribute 'name'\n",
            "INFO:tensorflow:Loss for final step: 0.31796658.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:14Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.55945s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:15\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.8030303, accuracy_baseline = 0.625, auc = 0.8679216, auc_precision_recall = 0.8527449, average_loss = 0.4203342, global_step = 153, label/mean = 0.375, loss = 0.4203342, precision = 0.7473684, prediction/mean = 0.38673538, recall = 0.7171717\n",
            "WARNING:tensorflow:Issue encountered when serializing resources.\n",
            "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n",
            "'_Resource' object has no attribute 'name'\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "{'accuracy': 0.8030303, 'accuracy_baseline': 0.625, 'auc': 0.8679216, 'auc_precision_recall': 0.8527449, 'average_loss': 0.4203342, 'label/mean': 0.375, 'loss': 0.4203342, 'precision': 0.7473684, 'prediction/mean': 0.38673538, 'recall': 0.7171717, 'global_step': 153}\n"
          ]
        }
      ],
      "source": [
        "in_memory_params = dict(params)\n",
        "in_memory_params['n_batches_per_layer'] = 1\n",
        "# In-memory input_fn does not use batching.\n",
        "def make_inmemory_train_input_fn(X, y):\n",
        "  y = np.expand_dims(y, axis=1)\n",
        "  def input_fn():\n",
        "    return dict(X), y\n",
        "  return input_fn\n",
        "train_input_fn = make_inmemory_train_input_fn(dftrain, y_train)\n",
        "\n",
        "# Train the model.\n",
        "est = tf.estimator.BoostedTreesClassifier(\n",
        "    feature_columns, \n",
        "    train_in_memory=True, \n",
        "    **in_memory_params)\n",
        "\n",
        "est.train(train_input_fn)\n",
        "print(est.evaluate(eval_input_fn))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TSZYqNcRuczV"
      },
      "source": [
        "## Model interpretation and plotting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "BjcfLiI3uczW"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns_colors = sns.color_palette('colorblind')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ywTtbBvBuczY"
      },
      "source": [
        "## Local interpretability\n",
        "Next you will output the directional feature contributions (DFCs) to explain individual predictions using the approach outlined in [Palczewska et al](https://arxiv.org/pdf/1312.1121.pdf) and by Saabas in [Interpreting Random Forests](http://blog.datadive.net/interpreting-random-forests/) (this method is also available in scikit-learn for Random Forests in the [`treeinterpreter`](https://github.com/andosa/treeinterpreter) package). The DFCs are generated with:\n",
        "\n",
        "`pred_dicts = list(est.experimental_predict_with_explanations(pred_input_fn))`\n",
        "\n",
        "(Note: The method is named experimental as we may modify the API before dropping the experimental prefix.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "colab_type": "code",
        "id": "TIL93B4sDRqE",
        "outputId": "af7164dd-062d-4cd2-f83e-66917e7a10d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpec8e696f', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ]
        }
      ],
      "source": [
        "pred_dicts = list(est.experimental_predict_with_explanations(eval_input_fn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "colab_type": "code",
        "id": "tDPoRx_ZaY1E",
        "outputId": "0d241cc4-4d11-41a7-d386-2b5de135348f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>264.0</td>\n",
              "      <td>-0.023851</td>\n",
              "      <td>0.095522</td>\n",
              "      <td>-0.154286</td>\n",
              "      <td>-0.076392</td>\n",
              "      <td>-0.057281</td>\n",
              "      <td>0.009408</td>\n",
              "      <td>0.541436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>264.0</td>\n",
              "      <td>0.007242</td>\n",
              "      <td>0.110388</td>\n",
              "      <td>-0.123509</td>\n",
              "      <td>-0.074560</td>\n",
              "      <td>-0.073093</td>\n",
              "      <td>0.139799</td>\n",
              "      <td>0.182828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>264.0</td>\n",
              "      <td>0.016631</td>\n",
              "      <td>0.093480</td>\n",
              "      <td>-0.065638</td>\n",
              "      <td>-0.046510</td>\n",
              "      <td>-0.045238</td>\n",
              "      <td>0.038365</td>\n",
              "      <td>0.236259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>deck</th>\n",
              "      <td>264.0</td>\n",
              "      <td>-0.016809</td>\n",
              "      <td>0.031755</td>\n",
              "      <td>-0.086020</td>\n",
              "      <td>-0.041980</td>\n",
              "      <td>-0.029283</td>\n",
              "      <td>0.003319</td>\n",
              "      <td>0.187737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>embark_town</th>\n",
              "      <td>264.0</td>\n",
              "      <td>-0.006789</td>\n",
              "      <td>0.028007</td>\n",
              "      <td>-0.053620</td>\n",
              "      <td>-0.014970</td>\n",
              "      <td>-0.013391</td>\n",
              "      <td>-0.003095</td>\n",
              "      <td>0.070267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fare</th>\n",
              "      <td>264.0</td>\n",
              "      <td>0.020249</td>\n",
              "      <td>0.088010</td>\n",
              "      <td>-0.308154</td>\n",
              "      <td>-0.028543</td>\n",
              "      <td>-0.008702</td>\n",
              "      <td>0.057093</td>\n",
              "      <td>0.228394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n_siblings_spouses</th>\n",
              "      <td>264.0</td>\n",
              "      <td>0.002551</td>\n",
              "      <td>0.028782</td>\n",
              "      <td>-0.177096</td>\n",
              "      <td>0.002736</td>\n",
              "      <td>0.003991</td>\n",
              "      <td>0.006956</td>\n",
              "      <td>0.135561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parch</th>\n",
              "      <td>264.0</td>\n",
              "      <td>-0.000049</td>\n",
              "      <td>0.009230</td>\n",
              "      <td>-0.075935</td>\n",
              "      <td>0.000389</td>\n",
              "      <td>0.000483</td>\n",
              "      <td>0.000871</td>\n",
              "      <td>0.050035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alone</th>\n",
              "      <td>264.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    count      mean       std  ...       50%       75%       max\n",
              "age                 264.0 -0.023851  0.095522  ... -0.057281  0.009408  0.541436\n",
              "sex                 264.0  0.007242  0.110388  ... -0.073093  0.139799  0.182828\n",
              "class               264.0  0.016631  0.093480  ... -0.045238  0.038365  0.236259\n",
              "deck                264.0 -0.016809  0.031755  ... -0.029283  0.003319  0.187737\n",
              "embark_town         264.0 -0.006789  0.028007  ... -0.013391 -0.003095  0.070267\n",
              "fare                264.0  0.020249  0.088010  ... -0.008702  0.057093  0.228394\n",
              "n_siblings_spouses  264.0  0.002551  0.028782  ...  0.003991  0.006956  0.135561\n",
              "parch               264.0 -0.000049  0.009230  ...  0.000483  0.000871  0.050035\n",
              "alone               264.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "\n",
              "[9 rows x 8 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create DFC Pandas dataframe.\n",
        "labels = y_eval.values\n",
        "probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])\n",
        "df_dfc = pd.DataFrame([pred['dfc'] for pred in pred_dicts])\n",
        "df_dfc.describe().T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EUKSaVoraY1C"
      },
      "source": [
        "A nice property of DFCs is that the sum of the contributions + the bias is equal to the prediction for a given example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Hd9VuizRaY1H"
      },
      "outputs": [],
      "source": [
        "# Sum of DFCs + bias == probabality.\n",
        "bias = pred_dicts[0]['bias']\n",
        "dfc_prob = df_dfc.sum(axis=1) + bias\n",
        "np.testing.assert_almost_equal(dfc_prob.values,\n",
        "                               probs.values)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tx5p4vEhuczg"
      },
      "source": [
        "Plot DFCs for an individual passenger. Let's make the plot nice by color coding based on the contributions' directionality and add the feature values on figure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "6z_Tq1Pquczj"
      },
      "outputs": [],
      "source": [
        "# Boilerplate code for plotting :)\n",
        "def _get_color(value):\n",
        "    \"\"\"To make positive DFCs plot green, negative DFCs plot red.\"\"\"\n",
        "    green, red = sns.color_palette()[2:4]\n",
        "    if value >= 0: return green\n",
        "    return red\n",
        "\n",
        "def _add_feature_values(feature_values, ax):\n",
        "    \"\"\"Display feature's values on left of plot.\"\"\"\n",
        "    x_coord = ax.get_xlim()[0]\n",
        "    OFFSET = 0.15\n",
        "    for y_coord, (feat_name, feat_val) in enumerate(feature_values.items()):\n",
        "        t = plt.text(x_coord, y_coord - OFFSET, '{}'.format(feat_val), size=12)\n",
        "        t.set_bbox(dict(facecolor='white', alpha=0.5))\n",
        "    from matplotlib.font_manager import FontProperties\n",
        "    font = FontProperties()\n",
        "    font.set_weight('bold')\n",
        "    t = plt.text(x_coord, y_coord + 1 - OFFSET, 'feature\\nvalue',\n",
        "    fontproperties=font, size=12)\n",
        "\n",
        "def plot_example(example):\n",
        "  TOP_N = 8 # View top 8 features.\n",
        "  sorted_ix = example.abs().sort_values()[-TOP_N:].index  # Sort by magnitude.\n",
        "  example = example[sorted_ix]\n",
        "  colors = example.map(_get_color).tolist()\n",
        "  ax = example.to_frame().plot(kind='barh',\n",
        "                          color=[colors],\n",
        "                          legend=None,\n",
        "                          alpha=0.75,\n",
        "                          figsize=(10,6))\n",
        "  ax.grid(False, axis='y')\n",
        "  ax.set_yticklabels(ax.get_yticklabels(), size=14)\n",
        "\n",
        "  # Add feature values.\n",
        "  _add_feature_values(dfeval.iloc[ID][sorted_ix], ax)\n",
        "  return ax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "colab_type": "code",
        "id": "Ht1P2-1euczk",
        "outputId": "a579726a-1126-4415-ddf0-44eac7e6ef13"
      },
      "outputs": [
        {
          "data": {
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v+IjygwQjybWAbsBdZWjnJoK5yu0JFr5/Klro7m+xa15zT+BigjVQH9iL2EVkHzCz/zGz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\n+D+VNoEp+8LX2gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot results.\n",
        "ID = 182\n",
        "example = df_dfc.iloc[ID]  # Choose ith example from evaluation set.\n",
        "TOP_N = 8  # View top 8 features.\n",
        "sorted_ix = example.abs().sort_values()[-TOP_N:].index\n",
        "ax = plot_example(example)\n",
        "ax.set_title('Feature contributions for example {}\\n pred: {:1.2f}; label: {}'.format(ID, probs[ID], labels[ID]))\n",
        "ax.set_xlabel('Contribution to predicted probability', size=14)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aPXgWyFcfzAc"
      },
      "source": [
        "The larger magnitude contributions have a larger impact on the model's prediction. Negative contributions indicate the feature value for this given example reduced the model's prediction, while positive values contribute an increase in the prediction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0swvlkZFaY1Z"
      },
      "source": [
        "You can also plot the example's DFCs compare with the entire distribution using a voilin plot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "zo7rNd1v_5e2"
      },
      "outputs": [],
      "source": [
        "# Boilerplate plotting code.\n",
        "def dist_violin_plot(df_dfc, ID):\n",
        "  # Initialize plot.\n",
        "  fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
        "\n",
        "  # Create example dataframe.\n",
        "  TOP_N = 8  # View top 8 features.\n",
        "  example = df_dfc.iloc[ID]\n",
        "  ix = example.abs().sort_values()[-TOP_N:].index\n",
        "  example = example[ix]\n",
        "  example_df = example.to_frame(name='dfc')\n",
        "\n",
        "  # Add contributions of entire distribution.\n",
        "  parts=ax.violinplot([df_dfc[w] for w in ix],\n",
        "                 vert=False,\n",
        "                 showextrema=False,\n",
        "                 widths=0.7,\n",
        "                 positions=np.arange(len(ix)))\n",
        "  face_color = sns_colors[0]\n",
        "  alpha = 0.15\n",
        "  for pc in parts['bodies']:\n",
        "      pc.set_facecolor(face_color)\n",
        "      pc.set_alpha(alpha)\n",
        "\n",
        "  # Add feature values.\n",
        "  _add_feature_values(dfeval.iloc[ID][sorted_ix], ax)\n",
        "\n",
        "  # Add local contributions.\n",
        "  ax.scatter(example,\n",
        "              np.arange(example.shape[0]),\n",
        "              color=sns.color_palette()[2],\n",
        "              s=100,\n",
        "              marker=\"s\",\n",
        "              label='contributions for example')\n",
        "\n",
        "  # Legend\n",
        "  # Proxy plot, to show violinplot dist on legend.\n",
        "  ax.plot([0,0], [1,1], label='eval set contributions\\ndistributions',\n",
        "          color=face_color, alpha=alpha, linewidth=10)\n",
        "  legend = ax.legend(loc='lower right', shadow=True, fontsize='x-large',\n",
        "                     frameon=True)\n",
        "  legend.get_frame().set_facecolor('white')\n",
        "\n",
        "  # Format plot.\n",
        "  ax.set_yticks(np.arange(example.shape[0]))\n",
        "  ax.set_yticklabels(example.index)\n",
        "  ax.grid(False, axis='y')\n",
        "  ax.set_xlabel('Contribution to predicted probability', size=14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PiLw2tlm_9aK"
      },
      "source": [
        "Plot this example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "colab_type": "code",
        "id": "VkCqraA2uczm",
        "outputId": "7959004b-aab8-4093-d72b-0da876acbb91"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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87e5DYtrN2trachdBBkgQGNUVcaortixzd7L5cL7zfAEcp+BhTScUp6AM520f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\n/gfYyJ26/428IgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dist_violin_plot(df_dfc, ID)\n",
        "plt.title('Feature contributions for example {}\\n pred: {:1.2f}; label: {}'.format(ID, probs[ID], labels[ID]))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TVJFM85SAWVq"
      },
      "source": [
        "Finally, third-party tools, such as [LIME](https://github.com/marcotcr/lime) and [shap](https://github.com/slundberg/shap), can also help understand individual predictions for a model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PnNXH6mZuczr"
      },
      "source": [
        "## Global feature importances\n",
        "\n",
        "Additionally, you might want to understand the model as a whole, rather than studying individual predictions. Below, you will compute and use:\n",
        "\n",
        "* Gain-based feature importances using `est.experimental_feature_importances`\n",
        "* Permutation importances\n",
        "* Aggregate DFCs using `est.experimental_predict_with_explanations`\n",
        "\n",
        "Gain-based feature importances measure the loss change when splitting on a particular feature, while permutation feature importances are computed by evaluating model performance on the evaluation set by shuffling each feature one-by-one and attributing the change in model performance to the shuffled feature.\n",
        "\n",
        "In general, permutation feature importance are preferred to gain-based feature importance, though both methods can be unreliable in situations where potential predictor variables vary in their scale of measurement or their number of categories and when features are correlated ([source](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-307)). Check out [this article](http://explained.ai/rf-importance/index.html) for an in-depth overview and great discussion on different feature importance types."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3ocBcMatuczs"
      },
      "source": [
        "### Gain-based feature importances"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gMaxCgPbBJ-j"
      },
      "source": [
        "Gain-based feature importances are built into the TensorFlow Boosted Trees estimators using `est.experimental_feature_importances`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "colab_type": "code",
        "id": "pPTxbAaeuczt",
        "outputId": "82215bfa-3228-4649-9bd6-3d665085b252"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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SJGlUnBGVJElSEwZRSZIkNWEQlSRJUhMGUUmSJDVhEJUkSVITBlFJkiQ1YRCVJElSEwZR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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "importances = est.experimental_feature_importances(normalize=True)\n",
        "df_imp = pd.Series(importances)\n",
        "\n",
        "# Visualize importances.\n",
        "N = 8\n",
        "ax = (df_imp.iloc[0:N][::-1]\n",
        "    .plot(kind='barh',\n",
        "          color=sns_colors[0],\n",
        "          title='Gain feature importances',\n",
        "          figsize=(10, 6)))\n",
        "ax.grid(False, axis='y')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GvfAcBeGuczw"
      },
      "source": [
        "### Average absolute DFCs\n",
        "You can also average the absolute values of DFCs to understand impact at a global level."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "colab_type": "code",
        "id": "JkvAWLWLuczx",
        "outputId": "abd46a1c-0641-4cee-9e10-012dd11635cf"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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1RO8aJEmSZhNHRCVJktSFQVSSJEldGEQlSZLUhfeIzkFb7Lofi5Yu612GJEnSpDgiKkmS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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot.\n",
        "dfc_mean = df_dfc.abs().mean()\n",
        "N = 8\n",
        "sorted_ix = dfc_mean.abs().sort_values()[-N:].index  # Average and sort by absolute.\n",
        "ax = dfc_mean[sorted_ix].plot(kind='barh',\n",
        "                       color=sns_colors[1],\n",
        "                       title='Mean |directional feature contributions|',\n",
        "                       figsize=(10, 6))\n",
        "ax.grid(False, axis='y')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Z0k_DvPLaY1o"
      },
      "source": [
        "You can also see how DFCs vary as a feature value varies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "colab_type": "code",
        "id": "ZcIfN1IpaY1o",
        "outputId": "bd0ab89d-42cb-4b15-d57c-287f80d19c4f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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TgodL4F1LKnnvRQu4ZtUCGldU2ZogWSoRC8RlqkwcQRmPWL+BXwNeFZHncK4P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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "FEATURE = 'fare'\n",
        "feature = pd.Series(df_dfc[FEATURE].values, index=dfeval[FEATURE].values).sort_index()\n",
        "ax = sns.regplot(feature.index.values, feature.values, lowess=True)\n",
        "ax.set_ylabel('contribution')\n",
        "ax.set_xlabel(FEATURE)\n",
        "ax.set_xlim(0, 100)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lbpG72ULucz0"
      },
      "source": [
        "### Permutation feature importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "6esOw1VOucz0",
        "outputId": "41f1b376-7319-4b13-d58f-38829f57def6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:18Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.56113s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:18\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.8030303, accuracy_baseline = 0.625, auc = 0.8679216, auc_precision_recall = 0.8527449, average_loss = 0.4203342, global_step = 153, label/mean = 0.375, loss = 0.4203342, precision = 0.7473684, prediction/mean = 0.38673538, recall = 0.7171717\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:19Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.57949s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:19\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.6060606, accuracy_baseline = 0.625, auc = 0.64355683, auc_precision_recall = 0.5400543, average_loss = 0.74337494, global_step = 153, label/mean = 0.375, loss = 0.74337494, precision = 0.47524753, prediction/mean = 0.39103043, recall = 0.4848485\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:20Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.58528s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:21\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.7916667, accuracy_baseline = 0.625, auc = 0.8624732, auc_precision_recall = 0.8392693, average_loss = 0.43363357, global_step = 153, label/mean = 0.375, loss = 0.43363357, precision = 0.7244898, prediction/mean = 0.38975066, recall = 0.7171717\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:21Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.55600s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:22\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.8068182, accuracy_baseline = 0.625, auc = 0.8674931, auc_precision_recall = 0.85280114, average_loss = 0.4206087, global_step = 153, label/mean = 0.375, loss = 0.4206087, precision = 0.75, prediction/mean = 0.38792592, recall = 0.72727275\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:22Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.54454s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:23\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.72727275, accuracy_baseline = 0.625, auc = 0.76737064, auc_precision_recall = 0.62659556, average_loss = 0.6019534, global_step = 153, label/mean = 0.375, loss = 0.6019534, precision = 0.6626506, prediction/mean = 0.3688063, recall = 0.5555556\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:24Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.53149s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:24\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.7878788, accuracy_baseline = 0.625, auc = 0.8389348, auc_precision_recall = 0.8278463, average_loss = 0.45054114, global_step = 153, label/mean = 0.375, loss = 0.45054114, precision = 0.7263158, prediction/mean = 0.3912348, recall = 0.6969697\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:25Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.54399s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:25\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.8030303, accuracy_baseline = 0.625, auc = 0.862565, auc_precision_recall = 0.84412414, average_loss = 0.42553493, global_step = 153, label/mean = 0.375, loss = 0.42553493, precision = 0.75268817, prediction/mean = 0.37500647, recall = 0.7070707\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:26Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.56776s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:26\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.8030303, accuracy_baseline = 0.625, auc = 0.8679216, auc_precision_recall = 0.8527449, average_loss = 0.4203342, global_step = 153, label/mean = 0.375, loss = 0.4203342, precision = 0.7473684, prediction/mean = 0.38673538, recall = 0.7171717\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:27Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.56329s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:28\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.79924244, accuracy_baseline = 0.625, auc = 0.8132232, auc_precision_recall = 0.7860318, average_loss = 0.4787808, global_step = 153, label/mean = 0.375, loss = 0.4787808, precision = 0.7613636, prediction/mean = 0.37704408, recall = 0.67676765\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-03-09T21:21:28Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpec8e696f/model.ckpt-153\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.60489s\n",
            "INFO:tensorflow:Finished evaluation at 2020-03-09-21:21:29\n",
            "INFO:tensorflow:Saving dict for global step 153: accuracy = 0.8030303, accuracy_baseline = 0.625, auc = 0.8360882, auc_precision_recall = 0.7940172, average_loss = 0.45960733, global_step = 153, label/mean = 0.375, loss = 0.45960733, precision = 0.7473684, prediction/mean = 0.38010252, recall = 0.7171717\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 153: /tmp/tmpec8e696f/model.ckpt-153\n"
          ]
        },
        {
          "data": {
            "image/png": 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GUUmSJHWxoHcHtPaW3Xw98088onc3JnTfkqN7d0GSJM1yzohKkiSpC4OoJEmSujCISpIk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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def permutation_importances(est, X_eval, y_eval, metric, features):\n",
        "    \"\"\"Column by column, shuffle values and observe effect on eval set.\n",
        "\n",
        "    source: http://explained.ai/rf-importance/index.html\n",
        "    A similar approach can be done during training. See \"Drop-column importance\"\n",
        "    in the above article.\"\"\"\n",
        "    baseline = metric(est, X_eval, y_eval)\n",
        "    imp = []\n",
        "    for col in features:\n",
        "        save = X_eval[col].copy()\n",
        "        X_eval[col] = np.random.permutation(X_eval[col])\n",
        "        m = metric(est, X_eval, y_eval)\n",
        "        X_eval[col] = save\n",
        "        imp.append(baseline - m)\n",
        "    return np.array(imp)\n",
        "\n",
        "def accuracy_metric(est, X, y):\n",
        "    \"\"\"TensorFlow estimator accuracy.\"\"\"\n",
        "    eval_input_fn = make_input_fn(X,\n",
        "                                  y=y,\n",
        "                                  shuffle=False,\n",
        "                                  n_epochs=1)\n",
        "    return est.evaluate(input_fn=eval_input_fn)['accuracy']\n",
        "features = CATEGORICAL_COLUMNS + NUMERIC_COLUMNS\n",
        "importances = permutation_importances(est, dfeval, y_eval, accuracy_metric,\n",
        "                                      features)\n",
        "df_imp = pd.Series(importances, index=features)\n",
        "\n",
        "sorted_ix = df_imp.abs().sort_values().index\n",
        "ax = df_imp[sorted_ix][-5:].plot(kind='barh', color=sns_colors[2], figsize=(10, 6))\n",
        "ax.grid(False, axis='y')\n",
        "ax.set_title('Permutation feature importance')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "E236y3pVEzHg"
      },
      "source": [
        "## Visualizing model fitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TrcQ-839EzZ6"
      },
      "source": [
        "Lets first simulate/create training data using the following formula:\n",
        "\n",
        "\n",
        "$$z=x* e^{-x^2 - y^2}$$\n",
        "\n",
        "\n",
        "Where \\(z\\) is the dependent variable you are trying to predict and \\(x\\) and \\(y\\) are the features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "e8woaj81GGE9"
      },
      "outputs": [],
      "source": [
        "from numpy.random import uniform, seed\n",
        "from scipy.interpolate import griddata\n",
        "\n",
        "# Create fake data\n",
        "seed(0)\n",
        "npts = 5000\n",
        "x = uniform(-2, 2, npts)\n",
        "y = uniform(-2, 2, npts)\n",
        "z = x*np.exp(-x**2 - y**2)\n",
        "xy = np.zeros((2,np.size(x)))\n",
        "xy[0] = x\n",
        "xy[1] = y\n",
        "xy = xy.T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "GRI3KHfLZsGP"
      },
      "outputs": [],
      "source": [
        "# Prep data for training.\n",
        "df = pd.DataFrame({'x': x, 'y': y, 'z': z})\n",
        "\n",
        "xi = np.linspace(-2.0, 2.0, 200),\n",
        "yi = np.linspace(-2.1, 2.1, 210),\n",
        "xi,yi = np.meshgrid(xi, yi)\n",
        "\n",
        "df_predict = pd.DataFrame({\n",
        "    'x' : xi.flatten(),\n",
        "    'y' : yi.flatten(),\n",
        "})\n",
        "predict_shape = xi.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "w0JnH4IhZuAb"
      },
      "outputs": [],
      "source": [
        "def plot_contour(x, y, z, **kwargs):\n",
        "  # Grid the data.\n",
        "  plt.figure(figsize=(10, 8))\n",
        "  # Contour the gridded data, plotting dots at the nonuniform data points.\n",
        "  CS = plt.contour(x, y, z, 15, linewidths=0.5, colors='k')\n",
        "  CS = plt.contourf(x, y, z, 15,\n",
        "                    vmax=abs(zi).max(), vmin=-abs(zi).max(), cmap='RdBu_r')\n",
        "  plt.colorbar()  # Draw colorbar.\n",
        "  # Plot data points.\n",
        "  plt.xlim(-2, 2)\n",
        "  plt.ylim(-2, 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KF7WsIcYGF_E"
      },
      "source": [
        "You can visualize the function. Redder colors correspond to larger function values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "colab_type": "code",
        "id": "WrxuqaaXGFOK",
        "outputId": "1f67e1e8-68c7-41f7-8ef7-a7658cdf4c6d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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BoVNeHvRa/ZwXJEQlC82IzUe37e+GnXGQ5e0RK+R44sGHeURy46Wzw/LJYgZw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UKEAN9UeBGxrpS20oMkj5keMhGCwPEYsgBnxUitPYbsBTpQ2sDhiv3cMLG3bwvpdkGAfplxSju9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kRIHcF34ZYlk7h6W00Jq6UCvt1zlibRz4K+06bqjbFNNrdjD5C59aVVhCRXYtJNlnUd0Slp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JqPGtJENGIX81wTFLC9jVT5iRo0hIjdVCdcGY3izdWhlMk10wJvTFoFev3jQ01NN0pJSM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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "zi = griddata(xy, z, (xi, yi), method='linear', fill_value='0')\n",
        "plot_contour(xi, yi, zi)\n",
        "plt.scatter(df.x, df.y, marker='.')\n",
        "plt.title('Contour on training data')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "hoANr0f2GFrM"
      },
      "outputs": [],
      "source": [
        "fc = [tf.feature_column.numeric_column('x'),\n",
        "      tf.feature_column.numeric_column('y')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "xVRWyoY3ayTK"
      },
      "outputs": [],
      "source": [
        "def predict(est):\n",
        "  \"\"\"Predictions from a given estimator.\"\"\"\n",
        "  predict_input_fn = lambda: tf.data.Dataset.from_tensors(dict(df_predict))\n",
        "  preds = np.array([p['predictions'][0] for p in est.predict(predict_input_fn)])\n",
        "  return preds.reshape(predict_shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uyPu5618GU7K"
      },
      "source": [
        "First let's try to fit a linear model to the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "colab_type": "code",
        "id": "zUIV2IVgGVSk",
        "outputId": "fe64df63-3253-4da2-8935-db2e01523ee1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpd4fqobc9\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpd4fqobc9', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:From /tensorflow-2.1.0/python3.6/tensorflow_core/python/feature_column/feature_column_v2.py:518: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.add_weight` method instead.\n",
            "WARNING:tensorflow:From /tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/optimizer_v2/ftrl.py:143: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpd4fqobc9/model.ckpt.\n",
            "INFO:tensorflow:loss = 0.023290718, step = 0\n",
            "INFO:tensorflow:global_step/sec: 267.329\n",
            "INFO:tensorflow:loss = 0.017512696, step = 100 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 312.355\n",
            "INFO:tensorflow:loss = 0.018098738, step = 200 (0.321 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.77\n",
            "INFO:tensorflow:loss = 0.019927984, step = 300 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 307.825\n",
            "INFO:tensorflow:loss = 0.01797011, step = 400 (0.327 sec)\n",
            "INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmpd4fqobc9/model.ckpt.\n",
            "INFO:tensorflow:Loss for final step: 0.019703189.\n"
          ]
        }
      ],
      "source": [
        "train_input_fn = make_input_fn(df, df.z)\n",
        "est = tf.estimator.LinearRegressor(fc)\n",
        "est.train(train_input_fn, max_steps=500);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "colab_type": "code",
        "id": "_u4WAcCqfbco",
        "outputId": "011908df-0263-4fa0-de41-48b3bb8c3c06"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n",
            "WARNING:tensorflow:Layer linear/linear_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
            "\n",
            "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
            "\n",
            "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
            "\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpd4fqobc9/model.ckpt-500\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ]
        },
        {
          "data": {
            "image/png": 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ZhfVr10hrOOZ/PA9HDh+W1nC8/LuXpBUSsCVDTD5KKSofoyOquUcV/QcgS1Sc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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_contour(xi, yi, predict(est))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XD_fMAUtSCSa"
      },
      "source": [
        "It's not a very good fit. Next let's try to fit a GBDT model to it and try to understand how the model fits the function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "colab_type": "code",
        "id": "-dHlKFlFgHDQ",
        "outputId": "05dd6873-76b9-4f74-80c4-8044a3ddbc0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp3jae7fgc/model.ckpt-222\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ]
        },
        {
          "data": {
            "image/png": 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IaFiYp8Otkdy8PIKCz7V0NW3ahE1bt+FwODwYVXneFEtD0aZFAv+Z/yXJG7eS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            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "n_trees = 37 #@param {type: \"slider\", min: 1, max: 80, step: 1}\n",
        "\n",
        "est = tf.estimator.BoostedTreesRegressor(fc, n_batches_per_layer=1, n_trees=n_trees)\n",
        "est.train(train_input_fn, max_steps=500)\n",
        "clear_output()\n",
        "plot_contour(xi, yi, predict(est))\n",
        "plt.text(-1.8, 2.1, '# trees: {}'.format(n_trees), color='w', backgroundcolor='black', size=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5WcZ9fubh1wT"
      },
      "source": [
        "As you increase the number of trees, the model's predictions better approximates the underlying function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cj8u3NCG-IKX"
      },
      "source": [
        "![](https://www.tensorflow.org/images/boosted_trees/boosted_trees_ntrees.gif)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SMKoEZnCdrsp"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ZSZUSrjXdw9g"
      },
      "source": [
        "In this tutorial you learned how to interpret Boosted Trees models using directional feature contributions and feature importance techniques. These techniques provide insight into how the features impact a model's predictions. Finally, you also gained intution for how a Boosted Tree model fits a complex function by viewing the decision surface for several models."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "boosted_trees_model_understanding.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
